收藏切换
IoT device identification method enhanced by packet-level traffic semantic features for large language models
收藏切换
PDF
Xinyu YIN, Fan SHI, Chengxi XU, Jiancheng ZHANG, Mingyi GE
Information Countermeasure Technology | 2025, 4(5) : 22 - 41
Less
收藏切换
Information Countermeasure Technology | 2025, 4(5): 22-41
Research Articles
IoT device identification method enhanced by packet-level traffic semantic features for large language models
Full
Xinyu YIN, Fan SHI, Chengxi XU, Jiancheng ZHANG, Mingyi GE
Affiliations
  • College of Electronic Engineering, National University of Defense Technology, Hefei 230037,China
doi: 10.12399/j.issn.2097-163x.2025.05.002
Outline
收藏切换

With the rapid popularization of Internet of Things(IoT)technology in various fields,network device identification has become a key link in the network security protection system. Real-time detection of IoT devices accessing the network is crucial for network management,security protection,and performance optimization. Accurately understanding network dynamics and identifying these IoT devices is a necessary prerequisite for effectively defending against hacker attacks. Traditional machine learning-based identification methods not only suffer from low efficiency,complex feature selection,and poor environmental transferability,but their accuracy also fails to meet the needs of practical protection. To address this issue,an IoT device identification method based on packet-level traffic semantic feature-enhanced large language models(LLM)was proposed. First,complex and heterogeneous IoT traffic was converted into universal packet-level traffic semantic features. Then,these packet-level traffic semantic features were used to fine-tune the LLM,enabling the LLM to automatically learn the potential traffic features of IoT devices and make device classification and identification decisions,thereby realizing end-to-end and efficient IoT device identification. Experimental results on the public datasets Aalto,UNSW,and hybrid CIC IoT datasets(2022,2023)show that the proposed method can effectively identify IoT devices based on packet-level traffic semantic features,and its the average identification accuracy can reach 99.99%,99.42%,and 98.83% respectively.

IoT  /  device identification  /  LLM  /  packet-level traffic semantic features
Xinyu YIN, Fan SHI, Chengxi XU, Jiancheng ZHANG, Mingyi GE. IoT device identification method enhanced by packet-level traffic semantic features for large language models[J]. Information Countermeasure Technology, 2025 , 4 (5) : 22 -41 . DOI: 10.12399/j.issn.2097-163x.2025.05.002
Year 2025 volume 4 Issue 5
PDF
82
37
Cite this Article
BibTeX
Article Info
doi: 10.12399/j.issn.2097-163x.2025.05.002
  • Receive Date:2025-07-03
  • Online Date:2026-04-23
Article Data
Affiliations
History
  • Received:2025-07-03
  • Revised:2025-09-09
Affiliations
    College of Electronic Engineering, National University of Defense Technology, Hefei 230037,China
References
Share
https://castjournals.cast.org.cn/joweb/xxdkjs/EN/10.12399/j.issn.2097-163x.2025.05.002
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT